{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ACTION_HIT = 0\n",
    "ACTION_STAND = 1\n",
    "ACTIONS = [ACTION_HIT, ACTION_STAND]\n",
    "MAX_SUM = 21\n",
    "PLAYER_POLICY = np.zeros(MAX_SUM + 1, dtype=np.int32)\n",
    "for i in range(12, 20):\n",
    "    PLAYER_POLICY[i] = ACTION_HIT\n",
    "PLAYER_POLICY[20] = ACTION_STAND\n",
    "PLAYER_POLICY[21] = ACTION_STAND\n",
    "\n",
    "BANKER_POLICY = np.zeros(MAX_SUM + 1, dtype=np.int32)\n",
    "for i in range(12, 17):\n",
    "    BANKER_POLICY[i] = ACTION_HIT\n",
    "for i in range(17, MAX_SUM + 1):\n",
    "    BANKER_POLICY[i] = ACTION_STAND\n",
    "\n",
    "# function form of target policy of player\n",
    "def target_player_policy(use_ace_player, player_sum, banker_card):\n",
    "    return PLAYER_POLICY[player_sum]\n",
    "\n",
    "# function form of behavior policy of player\n",
    "def behavior_player_policy(use_ace_player, player_sum, banker_card):\n",
    "    if np.random.binomial(1, 0.5) == 1:\n",
    "        return ACTION_STAND\n",
    "    return ACTION_HIT\n",
    "\n",
    "# get a new card\n",
    "CARD_NUM = 13\n",
    "def get_card():\n",
    "    card = np.random.randint(1, CARD_NUM + 1)\n",
    "    card = min(card, 10)\n",
    "    return card\n",
    "\n",
    "# get the value of a card (11 for ace).\n",
    "def card_value(card_id):\n",
    "    return 11 if card_id == 1 else card_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# play a game\n",
    "# @policy_player: specify policy for player\n",
    "# @initial_state: [whether player has a usable Ace, sum of player's cards, one card of dealer]\n",
    "# @initial_action: the initial action\n",
    "\n",
    "def play(policy_player, initial_state=None, initial_action=None):\n",
    "    player_sum = 0\n",
    "    player_trajectory = []\n",
    "    usable_ace_player = False\n",
    "    banker_card_01 = 0\n",
    "    banker_card_02 = 0\n",
    "    usable_ace_banker = False\n",
    "    if initial_state is None:\n",
    "        while player_sum < 12:\n",
    "            # if sum of player is less than 12, always hit\n",
    "            card = get_card()\n",
    "            player_sum += card_value(card)\n",
    "            # If the player's sum is larger than 21, he may hold one or two aces.\n",
    "            if player_sum > 21:\n",
    "                assert player_sum == 22\n",
    "                player_sum -= 10\n",
    "            else:\n",
    "                usable_ace_player |= (card == 1)\n",
    "        banker_card_01 = get_card()\n",
    "        banker_card_02 = get_card()\n",
    "    else:\n",
    "        usable_ace_player, player_sum, banker_card_01 = initial_state\n",
    "        banker_card_02 = get_card()\n",
    "    \n",
    "    # initial state of the game\n",
    "    state = [usable_ace_player, player_sum, banker_card_01]\n",
    "    # initialize banker's sum\n",
    "    banker_sum = card_value(banker_card_01) + card_value(banker_card_02)\n",
    "    usable_ace_banker = 1 in (banker_card_01, banker_card_02)\n",
    "    if banker_sum > 21:\n",
    "        # two ace in banker card\n",
    "        assert banker_sum == 22\n",
    "        banker_sum -= 10\n",
    "    assert banker_sum <= 21\n",
    "    assert player_sum <= 21\n",
    "\n",
    "    # game starts\n",
    "    # player's turn\n",
    "    while True:\n",
    "        if initial_action is not None:\n",
    "            action = initial_action\n",
    "            initial_action = None\n",
    "        else:\n",
    "            # get action based on current sum\n",
    "            action = policy_player(usable_ace_player, player_sum, banker_card_01)\n",
    "        player_trajectory.append(\n",
    "            [(usable_ace_player, player_sum, banker_card_01), action]\n",
    "        )\n",
    "        if action == ACTION_STAND:\n",
    "            break\n",
    "        card = get_card()\n",
    "        # Keep track of the ace count. the usable_ace_player flag is insufficient alone as it cannot\n",
    "        # distinguish between having one ace or two.\n",
    "        ace_count = int(usable_ace_player)\n",
    "        if card == 1:\n",
    "            ace_count += 1\n",
    "        player_sum += card_value(card)\n",
    "        while player_sum > 21 and ace_count:\n",
    "            ace_count -= 1\n",
    "            player_sum -= 10\n",
    "        if player_sum > 21:\n",
    "            return state, -1, player_trajectory\n",
    "        assert player_sum <= 21\n",
    "        usable_ace_player = (ace_count == 1)\n",
    "    # dealer's return\n",
    "    while True:\n",
    "        action = BANKER_POLICY[banker_sum]\n",
    "        if action == ACTION_STAND:\n",
    "            break\n",
    "        # get a new card\n",
    "        new_card = get_card()\n",
    "        ace_count = int(usable_ace_banker)\n",
    "        if new_card == 1:\n",
    "            ace_count += 1\n",
    "        banker_sum += card_value(new_card)\n",
    "\n",
    "        while banker_sum > 21 and ace_count:\n",
    "            ace_count -= 1\n",
    "            banker_sum -= 10\n",
    "        if banker_sum > 21:\n",
    "            return state, 1, player_trajectory\n",
    "        \n",
    "        usable_ace_banker = (1 == ace_count)\n",
    "    assert player_sum <= 21 and banker_sum <= 21\n",
    "    if player_sum > banker_sum:\n",
    "        return state, 1, player_trajectory\n",
    "    elif player_sum == banker_sum:\n",
    "        return state, 0, player_trajectory\n",
    "    else:\n",
    "        return state, -1, player_trajectory\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Monte Carlo Sample with On-Policy\n",
    "def monte_carlo_on_policy(episodes):\n",
    "    states_usable_ace = np.zeros((10, 10))\n",
    "    # initialze counts to 1 to avoid 0 being divided\n",
    "    states_usable_ace_count = np.ones((10, 10))\n",
    "    states_no_usable_ace = np.zeros((10, 10))\n",
    "    states_no_usable_ace_count = np.ones((10, 10))\n",
    "    for _ in tqdm(range(0, episodes)):\n",
    "        _, reward, player_trajectory = play(target_player_policy)\n",
    "        for (usable_ace_player, player_sum, banker_card_01), _ in player_trajectory:\n",
    "            player_sum -= 12\n",
    "            banker_card_01 -= 1\n",
    "            if usable_ace_player:\n",
    "                states_usable_ace[player_sum, banker_card_01] += reward\n",
    "                states_usable_ace_count[player_sum, banker_card_01] += 1\n",
    "            else:\n",
    "                states_no_usable_ace[player_sum, banker_card_01] += reward\n",
    "                states_no_usable_ace_count[player_sum, banker_card_01] += 1\n",
    "    return states_usable_ace / states_usable_ace_count, states_no_usable_ace / states_no_usable_ace_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def figure_5_1():\n",
    "    states_usable_ace_1, states_no_usable_ace_1 = monte_carlo_on_policy(10000)\n",
    "    states_usable_ace_2, states_no_usable_ace_2 = monte_carlo_on_policy(500000)\n",
    "    states = [\n",
    "        states_usable_ace_1,\n",
    "        states_usable_ace_2,\n",
    "        states_no_usable_ace_1,\n",
    "        states_no_usable_ace_2\n",
    "    ]\n",
    "    titles = ['Usable Ace, 10000 Episodes',\n",
    "              'Usable Ace, 500000 Episodes',\n",
    "              'No Usable Ace, 10000 Episodes',\n",
    "              'No Usable Ace, 500000 Episodes']\n",
    "    \n",
    "\n",
    "    _, axes = plt.subplots(2, 2, figsize=(40, 30))\n",
    "    plt.subplots_adjust(wspace=0.1, hspace=0.2)\n",
    "    axes = axes.flatten()\n",
    "\n",
    "    for state, title, axis in zip(states, titles, axes):\n",
    "        fig = sns.heatmap(np.flipud(state), cmap=\"YlGnBu\", ax=axis, xticklabels=range(1, 11),\n",
    "                          yticklabels=list(reversed(range(12, 22))))\n",
    "        fig.set_ylabel('player sum', fontsize=30)\n",
    "        fig.set_xlabel('banker showing', fontsize=30)\n",
    "        fig.set_title(title, fontsize=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:00<00:00, 34077.04it/s]\n",
      "100%|██████████| 500000/500000 [00:09<00:00, 51612.70it/s]\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 4000x3000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure_5_1()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Monte Carlo with Exploring Starts\n",
    "def monte_carlo_es(episodes):\n",
    "    # (playerSum, dealerCard, usableAce, action)\n",
    "    state_action_values = np.zeros([10, 10, 2, 2])\n",
    "    state_action_pair_count = np.ones([10, 10, 2, 2])\n",
    "\n",
    "    # behavior policy is greedy\n",
    "    def behavior_policy(usable_ace, player_sum, banker_card):\n",
    "        usable_ace = int(usable_ace)\n",
    "        player_sum -= 12\n",
    "        banker_card -= 1\n",
    "        values_ = state_action_values[player_sum, banker_card, usable_ace, :] / \\\n",
    "            state_action_pair_count[player_sum, banker_card, usable_ace, :]\n",
    "        return np.random.choice([action_ for action_, value_ in enumerate(values_) if value_ == np.max(values_)])\n",
    "    \n",
    "    for episode in tqdm(range(episodes)):\n",
    "        initial_state = [bool(np.random.choice([0, 1])),\n",
    "                         np.random.choice(range(12, 22)),\n",
    "                         np.random.choice(range(1, 11))]\n",
    "        initial_action = np.random.choice(ACTIONS)\n",
    "        current_policy = behavior_policy if episode else target_player_policy\n",
    "        _, reward, trajectory = play(current_policy, initial_state, initial_action)\n",
    "        first_visit_check = set()\n",
    "        for (usable_ace_player, player_sum, banker_card_01), action in trajectory:\n",
    "            player_sum -= 12\n",
    "            banker_card_01 -= 1\n",
    "            usable_ace_player = int(usable_ace_player)\n",
    "            state_action = (usable_ace_player, player_sum, banker_card_01, action)\n",
    "            if state_action in first_visit_check:\n",
    "                continue\n",
    "            first_visit_check.add(state_action)\n",
    "            state_action_values[player_sum, banker_card_01, usable_ace_player, action] += reward\n",
    "            state_action_pair_count[player_sum, banker_card_01, usable_ace_player, action] += 1\n",
    "\n",
    "    return state_action_values / state_action_pair_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500000/500000 [00:38<00:00, 12911.98it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 4000x3000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "def figure_5_2():\n",
    "    state_action_values = monte_carlo_es(500000)\n",
    "\n",
    "    state_value_no_usable_ace = np.max(state_action_values[:, :, 0, :], axis=-1)\n",
    "    state_value_usable_ace = np.max(state_action_values[:, :, 1, :], axis=-1)\n",
    "\n",
    "    # get the optimal policy\n",
    "    action_no_usable_ace = np.argmax(state_action_values[:, :, 0, :], axis=-1)\n",
    "    action_usable_ace = np.argmax(state_action_values[:, :, 1, :], axis=-1)\n",
    "\n",
    "    images = [action_usable_ace,\n",
    "              state_value_usable_ace,\n",
    "              action_no_usable_ace,\n",
    "              state_value_no_usable_ace]\n",
    "\n",
    "    titles = ['Optimal policy with usable Ace',\n",
    "              'Optimal value with usable Ace',\n",
    "              'Optimal policy without usable Ace',\n",
    "              'Optimal value without usable Ace']\n",
    "\n",
    "    _, axes = plt.subplots(2, 2, figsize=(40, 30))\n",
    "    plt.subplots_adjust(wspace=0.1, hspace=0.2)\n",
    "    axes = axes.flatten()\n",
    "\n",
    "    for image, title, axis in zip(images, titles, axes):\n",
    "        fig = sns.heatmap(np.flipud(image), cmap=\"YlGnBu\", ax=axis, xticklabels=range(1, 11),\n",
    "                          yticklabels=list(reversed(range(12, 22))))\n",
    "        fig.set_ylabel('player sum', fontsize=30)\n",
    "        fig.set_xlabel('dealer showing', fontsize=30)\n",
    "        fig.set_title(title, fontsize=30)\n",
    "figure_5_2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Monte Carlo Sample with Off-Policy\n",
    "def monte_carlo_off_policy(episodes):\n",
    "    initial_state = [True, 13, 2]\n",
    "    rhos = []\n",
    "    returns = []\n",
    "    for i in range(0, episodes):\n",
    "        _, reward, player_trajectory = play(behavior_player_policy, initial_state=initial_state)\n",
    "\n",
    "\n",
    "        # get the importance ratio\n",
    "        numerator = 1.0\n",
    "        denominator = 1.0\n",
    "        for (usable_ace_player, player_sum, banker_card_01), action in player_trajectory:\n",
    "            if action == target_player_policy(usable_ace_player, player_sum, banker_card_01):\n",
    "                denominator *= 0.5\n",
    "            else:\n",
    "                numerator = 0.0\n",
    "                break\n",
    "        rho = numerator / denominator\n",
    "        rhos.append(rho)\n",
    "        returns.append(reward)\n",
    "    \n",
    "    rhos = np.asarray(rhos)\n",
    "    returns = np.asarray(returns)\n",
    "    weighted_returns = rhos * returns\n",
    "    weighted_returns = np.add.accumulate(weighted_returns)\n",
    "    rhos = np.add.accumulate(rhos)\n",
    "\n",
    "    ordinary_sampling = weighted_returns / np.arange(1, episodes + 1)\n",
    "    with np.errstate(divide='ignore', invalid='ignore'):\n",
    "        weighted_sampling = np.where(rhos != 0, weighted_returns / rhos, 0)\n",
    "    return ordinary_sampling, weighted_sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def figure_5_3():\n",
    "    true_value = -0.27726\n",
    "    episodes = 10000\n",
    "    runs = 100\n",
    "    error_ordinary = np.zeros(episodes)\n",
    "    error_weighted = np.zeros(episodes)\n",
    "    for i in tqdm(range(0, runs)):\n",
    "        ordinary_sampling_, weighted_sampling_ = monte_carlo_off_policy(episodes)\n",
    "        # get the squared error\n",
    "        error_ordinary += np.power(ordinary_sampling_ - true_value, 2)\n",
    "        error_weighted += np.power(weighted_sampling_ - true_value, 2)\n",
    "    error_ordinary /= runs\n",
    "    error_weighted /= runs\n",
    "\n",
    "    plt.plot(np.arange(1, episodes + 1), error_ordinary, color='green', label='Ordinary Importance Sampling')\n",
    "    plt.plot(np.arange(1, episodes + 1), error_weighted, color='red', label='Weighted Importance Sampling')\n",
    "    plt.ylim(-0.1, 5)\n",
    "    plt.xlabel('Episodes (log scale)')\n",
    "    plt.ylabel(f'Mean square error\\n(average over {runs} runs)')\n",
    "    plt.xscale('log')\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:18<00:00,  5.46it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure_5_3()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "ACTION_BACK = 0\n",
    "ACTION_END = 1\n",
    "ACTIONS = [ACTION_BACK, ACTION_END]\n",
    "def behavior_policy():\n",
    "    return np.random.choice(ACTIONS)\n",
    "    # return np.random.binomial(1, 0.5)\n",
    "\n",
    "def target_policy():\n",
    "    return ACTION_BACK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def play():\n",
    "    trajectory = []\n",
    "    while True:\n",
    "        action = behavior_policy()\n",
    "        trajectory.append(action)\n",
    "        if action == ACTION_END:\n",
    "            return 0, trajectory\n",
    "        if np.random.binomial(1, 0.9) == 0:\n",
    "            return 1, trajectory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def figure_5_4():\n",
    "    runs = 10\n",
    "    episodes = 100000\n",
    "    for run in range(runs):\n",
    "        rewards = []\n",
    "        for episode in range(0, episodes):\n",
    "            reward, trajectory = play()\n",
    "            if trajectory[-1] == ACTION_END:\n",
    "                rho = 0\n",
    "            else:\n",
    "                rho = 1.0 / pow(0.5, len(trajectory))\n",
    "            rewards.append(rho * reward)\n",
    "        rewards = np.add.accumulate(rewards)\n",
    "        estimations = np.asarray(rewards) / np.arange(1, episodes + 1)\n",
    "        plt.plot(estimations)\n",
    "    plt.xlabel('Episodes (log scale)')\n",
    "    plt.ylabel('Ordinary Importance Sampling')\n",
    "    plt.xscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
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